1. Infrastructure-less indoor location guidance

3.  Emergency Response – Fire ◦ Unknown environment ◦ No infrastructure ◦ Need for navigation  Locating Things – Walmart/ Old people’s home ◦ Low cost infrastructure ◦ Quick and easy to deploy and maintain ◦ Need for navigation Navigation Leading people to the point of interest is sufficient, as opposed to knowing it’s absolute location on a map. Navigation Leading people to the point of interest is sufficient, as opposed to knowing it’s absolute location on a map.

4.  Existing location systems Camera (Slam) Resource intensive Privacy GPS-like Range Based Ultrasound/UWB (Slam) Need infrastructure Signature Based Wi-Fi Coarse-grained Calibration

5.  Self-configuring indoor navigation system  No pre-existing infrastructure needed  No manual calibration required

6.  Signatures  Clusters  Local Compass Signatures  Virtual Maps

7. Start: front door, 1 st floor Landmark: stairs Destination: Pei’s office Landmark: sofa

8.  Round-trip time-of-flight readings from arbitrarily placed anchor nodes.  {r1, r2, r3, r4, …, rN}  RToF readings are stable over time for a particular room geometry but show high error

11.  Signatures can be clustered by a distance threshold to create virtual landmarks.

13. Given current reading and direction, the belief of in Cluster Possibility of one step away from Cluster in direction ending up in Cluster

15.  The compass reading differs in different environment  What we need is relative direction ( like, ‘turn left’ )

16. Result Analysis

17.  Using relation between real distance and single signature reading to get complete signature  Using generated signature to get distribution table for the possibility of certain reading belongs to certain cluster  Cluster  Navigation  Kmeans Re-cluster

22.  Average Distance Error: to measure the accuracy of the guiding system  Average Step: to measure how well the guidance is on choosing path

23.  Number of Anchors ◦ At least 4 ◦ Tested from 4 to 12  Distribution Table (the clusters size) ◦ Tested from 0.5 to 3

28.  Collecting Ranging Signatures and Compass Readings every 10 centimeters ◦ 20 ranging signatures for one point ◦ 1 Compass reading heading opposite to the door  Randomly pick 3000 Readings as training trail  Filtering readings in signature by their stand deviation  Using subset of the signature for clustering

32.  Ranging Test ◦ How long can it rang? ◦ Where to put anchors?  Clustering Test ◦ Can area across racks be distinguished? ◦ Can area alone the racks be distinguished?

35. Connect Base to the laptop Use Matlab serial port get data directly

36. Anchor

37. Base and Node align vertically

43. First Rack Second Rack

48.  Using sub-set of signature in Clustering  Comparing 2 readings’ overlapped signature readings number ◦ If > valid_sig_threshold : use corresponding distribution table to determine if they are in same cluster ◦ Else : considering them in 2 different clusters